feasibility and accuracy of dual layer spectral detector computed tomography for quantification of gadolinium a phantom study

CONTRAST MEDIAFeasibility and accuracy of dual-layer spectral detector computed tomography for quantification of gadolinium: a phantom study Robbert W.. This article is published with op

CONTRAST MEDIA

Feasibility and accuracy of dual-layer spectral detector computed tomography for quantification of gadolinium: a phantom study

Robbert W van Hamersvelt1&Martin J Willemink1&Pim A de Jong1&Julien Milles2&

Alain Vlassenbroek3&Arnold M R Schilham1&Tim Leiner1

Received: 11 August 2016 / Revised: 12 December 2016 / Accepted: 3 January 2017

# The Author(s) 2017 This article is published with open access at Springerlink.com

Abstract

Objectives The aim of this study was to evaluate the

feasibil-ity and accuracy of dual-layer spectral detector CT (SDCT) for

the quantification of clinically encountered gadolinium

concentrations

Methods The cardiac chamber of an anthropomorphic

tho-racic phantom was equipped with 14 tubular inserts

con-taining different gadolinium concentrations, ranging from

0 to 26.3 mg/mL (0.0, 0.1, 0.2, 0.4, 0.5, 1.0, 2.0, 3.0, 4.0,

5.1, 10.6, 15.7, 20.7 and 26.3 mg/mL) Images were

ac-quired using a novel 64-detector row SDCT system at 120

and 140 kVp Acquisitions were repeated five times to

assess reproducibility Regions of interest (ROIs) were

drawn on three slices per insert A spectral plot was

ex-tracted for every ROI and mean attenuation profiles were

fitted to known attenuation profiles of water and pure

gadolinium using in-house-developed software to

calcu-late gadolinium concentrations

Results At both 120 and 140 kVp, excellent correlations

between scan repetitions and true and measured

gadolin-ium concentrations were found (R > 0.99, P < 0.001;

ICCs > 0.99, CI 0.99–1.00) Relative mean measurement

errors stayed below 10% down to 2.0 mg/mL true

gadolinium concentration at 120 kVp and below 5% down

to 1.0 mg/mL true gadolinium concentration at 140 kVp Conclusion SDCT allows for accurate quantification of gad-olinium at both 120 and 140 kVp Lowest measurement errors were found for 140 kVp acquisitions

Key Points

• Gadolinium quantification may be useful in patients with contraindication to iodine

• Dual-layer spectral detector CT allows for overall accurate quantification of gadolinium

• Interscan variability of gadolinium quantification using SDCT material decomposition is excellent

Keywords Dual-energy CT Dual-layer spectral detector CT Contrast media Gadolinium Material decomposition

Abbreviations

ICC Intraclass correlation coefficient

NIST National Institute of Standards and Technology

SDCT Dual-layer spectral detector computed tomography

* Robbert W van Hamersvelt

R.W.vanHamersvelt-3@umcutrecht.nl

1

Department of Radiology, University Medical Center Utrecht,

P.O Box 85500, 3508 GA Utrecht, The Netherlands

2 CT Clinical Science, Philips HealthCare, Best, The Netherlands

3

CT Clinical Science, Philips HealthCare, Brussels, Belgium

DOI 10.1007/s00330-017-4737-8

Material decomposition imaging (MDI) using dual-energy

computed tomography (DECT) was first described by

Hounsfield in 1973 [1] Different materials, which cannot be

distinguished on the basis of attenuation number, can be

dis-tinguished with the use of material decomposition algorithms

using DECT acquisitions [2–4] Materials with high atomic

numbers, such as iodine (Z = 53) and gadolinium (Z = 64),

show characteristic high attenuation profiles at low energies

owing to a substantial contribution of the photoelectric effect

to the attenuation [5] MDI uses these characteristic

attenua-tion profiles to differentiate these contrast agents from other

materials MDI has not been widely applied in clinical practice

until recently Over the past few years several CT vendors

have made DECT commercially available for daily clinical

practice Recently a novel DECT technique has become

com-mercially available, which uses a single tube with a dual-layer

detector capable of differentiating between low and high

en-ergy X-ray photons, and is further investigated in this study

One of the most widely researched MDI applications is

quan-titative mapping of iodine distribution in tissues The resulting

maps can be used as a surrogate for tissue perfusion Early

evi-dence has shown the clinical capability of iodine quantification

with DECT at a specified time point for the detection of

myocar-dial [6–12] and pulmonary perfusion defects [13–16] In

addi-tion, DECT iodine mapping is capable of tumour mass

charac-terization and therapy response assessment [17–19] However,

iodine contrast administration, while safe in most patients, is

associated with contrast-induced allergic reactions and

nephrop-athy which can cause acute renal dysfunction [20,21] and

sig-nificant morbidity and mortality, especially in high-risk patients

[22,23] In patients with contraindications to iodinated contrast

media, gadolinium-enhanced magnetic resonance (MR)

angiog-raphy can be used as an alternative However, depending on the

indication, MR angiography may have poor diagnostic value

compared to (DE)CT angiography Gadolinium-based CT has

been used off-label in higher doses as an alternative for

conven-tional CT angiography with diagnostic image quality [24,25]

With the use of DECT, higher attenuation can be achieved at low (monochromatic) energies, which could enable the use of much lower gadolinium concentrations [26,27] In addition, accurate gadolinium quantification using DECT could allow for a quanti-tative evaluation of contrast agent distribution in tissue as a sur-rogate for tissue perfusion using MDI Therefore, accurate gad-olinium quantification combined with increased attenuation could potentially open up the possibility for gadolinium as an alternative contrast agent for DECT imaging in patients with contraindications to iodinated contrast media

In several studies the feasibility of gadolinium-enhanced DECT has been reported in phantom and animal models [28–31] These studies described the capability of spectral dif-ferentiation and visualisation [28–30] and accuracy of quantifi-cation [31] of gadolinium using DECT However, the accuracy

of gadolinium quantification using the novel dual-layer spectral detector CT system (SDCT) is unknown Therefore, the aim of the current study was to evaluate the feasibility and accuracy of gadolinium quantification using a SDCT system

Materials and methods Phantom design

An anthropomorphic chest phantom (QRM GmbH, Moehrendorf, Germany) was used The phantom resembles a chest with corresponding X-ray attenuation behaviour The phan-tom has a cylindrical cardiac chamber in which a plastic holder was placed (Fig.1) Three plastic holders were made, two consisting of five tubular inserts, and one consisting of three tubular inserts with surrounding 2% agar gel solution In addi-tion, a plastic holder with one tubular insert containing water with surrounding 2% agar gel solution served as control The fourteen 32-mL tubular inserts contained different concentrations of the gadolinium-based contrast agent gadobutrol (Gadovist 1.0, Bayer Healthcare, Berlin, Germany) One millilitre of this con-trast agent contains 157.25 mg gadolinium Different amounts of gadobutrol were diluted in water, resulting in the following

Fig 1 Phantom setup a

Anthropomorphic thoracic

phantom with a plastic holder

placed in the cardiac chamber b

Representative plastic holder

filled with 5 tubular inserts, with

surrounding 2% agar gel solution

concentrations of gadolinium: 0.0, 0.1, 0.2, 0.4, 0.5, 1.0, 2.0, 3.0,

4.0, 5.1, 10.6, 15.7, 20.7 and 26.3 mg/mL, which is equivalent to

0.000, 0.001, 0.002, 0.002, 0.003, 0.006, 0.013, 0.019, 0.026,

0.032, 0.068, 0.100, 0.132 and 0.167 mmol/mL, respectively

Concentrations were chosen to mimic an estimated clinical

range of gadolinium concentrations encountered after

injec-tion of 0.1–0.2 mmol of gadolinium per kilogram Strich et al

[32] measured percentage dose of gadolinium-based contrast

agent per gram of tissue in healthy rabbit organs 5 min after

admission, resulting in the following percentages: 0.052%/g

heart, 0.073%/g lungs, 0.037%/g liver, 0.037%/g spleen and

0.250%/g kidney On the basis of these percentages, an

esti-mation of gadolinium concentrations encountered at each

or-gan can be calculated At 31.5 mg/kg bodyweight (equal to

0.2 mmol/kg) gadolinium administration, a human subject of

70 kg would receive a total of 2201.5 mg gadolinium On the

basis of the percentages determined by Strich and colleagues,

gadolinium distribution in the heart 5 min after injection

would be 0.00052 × 2201.5 mg, or 1.15 mg per gram

myocar-dium Myocardial muscle has a specific gravity of 1.05 g/mL

[33], implicating an estimated gadolinium concentration

en-countered in the myocardium of 1.15 mg/g × 1.05 g/mL, or

1.21 mg/mL Using the aforementioned distribution

percent-ages these calculations can also be applied to the lungs, liver,

spleen and kidney, with a calculated estimated specific gravity

(weight/volume) of 1.34, 1.01, 0.71 and 0.85 g/mL,

respec-tively [34–36] Thus, it is to be expected that gadolinium

concentrations of 2.15, 0.82, 0.58 and 4.67 mg/mL are

en-countered in healthy lung, liver, spleen and kidney tissue,

respectively These concentrations are in the range of

concen-trations evaluated in this study As it is expected that in tissue

with a perfusion defect lower concentrations of gadolinium

will be encountered, we also evaluated ultra-low

concentra-tions of gadolinium down to 0.1 mg/mL

Image acquisition

Images were acquired using the newest generation 64-detector

row SDCT system (iQon Spectral CT, Philips Healthcare,

Best, the Netherlands) This system uses a single X-ray tube

and a dual-layer detector The detector separates the X-ray

beam into low (upper layer) and high (lower layer) energy

data, which is used to reconstruct spectral-based images

(SBI) The SBI contain the raw data of both layers and are

used to reconstruct any dual-energy image and/or analysis In

addition, by combining the output of both layers, a

conven-tional image is reconstructed from the data The phantom was

imaged in spiral mode at 120 and 140 kVp The tube current–

time product was set to a fixed value of 200 mAs, resulting in

a volumetric CT dose index (CTDIvol) of 18.4 and 26.5 mGy

for 120 and 140 kVp acquisitions, respectively The following

parameters were used: detector collimation 64 × 0.625 mm,

rotation time 0.4 s and pitch 1.046 At both tube voltages,

acquisitions were repeated five times with small displace-ments between each acquisition to take into account interscan variation Thus, the phantom was translated a few millimetres

in the left–right direction, as well as along the z-axis of the CT scanner After the five repetitions, the phantom was set back to the starting position

Image reconstruction The raw projection data from both detector layers were auto-matically reconstructed into SBI Subsequently, MDI was per-formed in the projection domain, which efficiently eliminates beam hardening artefacts [37] All images were reconstructed with standard chest reconstruction filter B and spectral level 3 Spectral is a model-based iterative reconstruction developed for the SDCT, it is an equivalent to iterative model-based reconstruction (IMR) Spectral consists of six levels, whereby

a higher spectral level implies more noise reduction Slice thickness and increment were both 1 mm The reconstructed images were evaluated on a dedicated workstation using the Spectral CT Viewer (IntelliSpace Portal v6.5.0.02080, Philips Healthcare, Best, the Netherlands)

Image analysis and gadolinium quantification

On three different slices of each data set a region of interest (ROI) with a fixed size of 225 mm2was drawn in the centre of each insert (Fig.2a) Subsequently spectral plots of every ROI were obtained, in which mean Hounsfield units (HU) were plotted as a function of different energy levels expressed in kilo electron volt (keV) (Fig.2b) These mean HU values of the spectral plots were extracted in steps of 10 keVand used as

an input for the analysis The currently used SDCT system uses traditional integrated detectors at two energy spectra and is therefore not able to image and/or quantify a material-specific K-edge [37] Materials with a K-edge within the SDCT range (40–200 keV) will not show a discontinuity in their attenuation function on the SDCT spectral plot When evaluating the mean attenuation across monochromatic ener-gies, this does not pose a problem and therefore the whole energy spectrum can be used (40–200 keV) However, for the quantitative analyses of gadolinium concentrations a com-parison is made with the attenuation profile of pure

gadolini-um which does contain the discontinuity in their attenuation function at the K-edge Therefore, to take into account the non-linear energy dependency close to the K-edge of gadolin-ium (50.2 keV), only the energy range from 70 to 200 keV was used for the quantitative analysis With in-house-developed software, attenuation profiles were reconstructed from the provided mean HU, and gadolinium concentrations were calculated by fitting combinations of known attenuation profiles of pure gadolinium and water to the

reconstruct-ed attenuation profiles (Fig.2c) For each ROI drawn in the

phantom, the in-house-developed software assumed that all

voxels within this ROI were composed of only gadolinium

and water and that the sum of these fractions added up to

100% Known attenuation profiles of pure gadolinium and

water were obtained from the National Institute of

Standards and Technology (NIST) database [38] Therefore,

no calibration scans with water and/or gadolinium

concentra-tions were needed For all thirteen different gadolinium

con-centrations, 15 measurements were performed at both 120 and

140 kVp (three slices, five repetitions) In addition, 30

mea-surements (15 at both 120 and 140 kVp) were performed on

the control phantom Gadolinium concentrations were

calcu-lated for each measurement

Attenuation coefficient

CT attenuation during injection of low gadolinium concentra-tions (i.e 0.1–0.2 mmol/kg bodyweight) will generally lead to lower HU values compared to the use of iodinated contrast agents [24,25] To investigate the ability of SDCT to visually identify an increase in HU values due to the presence of a gadolinium-containing contrast agent we extracted mean at-tenuation coefficients across monochromatic energies (40–

200 keV) for the different gadolinium concentrations used in this study (Fig.3)

Statistical analysis

To evaluate the quantification accuracy of gadolinium concen-trations, we defined measurement errors in milligrams per millilitre and relative measurement errors in percentages Measurement errors were calculated by subtracting true gad-olinium concentrations from the measured gadgad-olinium con-centrations Subsequently, relative measurement errors (%) were calculated as follows:

Relative measurement errorð Þ%

mg mL

true gadolinium concentration mg

mL

   100 %ð Þ

All measurement error analyses were performed sepa-rately for 120 and 140 kVp In addition, sub-analyses were done for each concentration The Shapiro–Wilk test was used to identify normally distributed data For each con-centration, statistical differences of measurement errors between 120 and 140 kVp were analysed using paired t test for normally distributed data A Bonferroni corrected

P < 0.004 (0.05/number of comparisons) was considered significant Pearson’s correlation coefficient was used to evaluate correlations between measured and true gadolin-ium concentrations at different tube voltages and for each scan repetition In addition, reproducibility was evaluated

To define agreement of results, the two-way random single measure intraclass correlation coefficient (ICC) with cor-responding confidence interval (CI) was used for all pos-sible two-way interactions ICCs between 0.61 and 0.80 were considered good and ICCs greater than 0.80 excel-lent [39] Measurement interscan variabilities of all scan repetitions were plotted in one single plot by using a mod-ified Bland–Altman plot described by Jones et al [40] In this figure the measurement differences of every measure-ment compared to the mean measuremeasure-ment of all scans are plotted against the mean measurement of all scans As described by Jones et al., the limits of agreement were calculated as mean ± 1.96 × SD, where the SD is an

Fig 2 Axial CT image and measurements a Axial conventional SDCT

image of the phantom with 5 tubular inserts, surrounded by 2% agar gel.

ROIs with a fixed area of 225 mm 2 drawn in the centre of each insert b A

spectral plot of each ROI was conducted, showing mean Hounsfield units

plotted against energy in keV Hounsfield unit values of the spectral plots

were extracted in increments of 10 keV c Using in-house-developed

software, we reconstructed attenuation profiles between 70 to 200 keV

from the extracted Hounsfield units, and a combination of known

attenuation profiles of pure gadolinium and water was fitted to the

reconstructed attenuation profile This case concerns ROI S3, containing

5.1 mg of gadolinium per millilitre

estimate of the standard deviation for all observers [40].

Values are listed as mean with standard deviation (SD),

unless stated otherwise A P value less than 0.05 was used

to indicate statistical significance IBM SPSS version 21.0

(IBM corp., Armonk, New York, USA) was used for

sta-tistical analyses

Results

Measurements of the water-filled insert, which served as

control, yielded 0.0 ± 0.0 mg/mL with a measurement

error of 0.0 ± 0.0 mg/mL for all measurements To avoid

influence on measurement accuracy, these control

measurements were not included in further statistical analyses

Accuracy and reproducibility

At both 120 and 140 kVp, excellent correlations (R > 0.99,

P < 0.001; ICCs > 0.99, CI 0.99–1.00) were found between true and measured gadolinium concentrations for each scan repetition In addition, reproducibility between all scan repe-titions was excellent (R > 0.99, P < 0.001; ICCs > 0.99, CI 0.99–1.00) The interscan agreement is displayed in Fig.4a for 120 kVp and Fig 4b for 140 kVp Because excellent correlations were found, all scan repetitions were analysed combined together in subsequent analyses

0 250 500 750 1000 1250 1500 1750 2000 2250 2500

40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200

40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200

Energy (keV)

26.3 mg/mL 20.7 mg/mL 15.7 mg/mL 10.6 mg/mL 5.1 mg/mL 4.0 mg/mL 3.0 mg/mL 2.0 mg/mL 1.0 mg/mL 0.5 mg/mL 0.4 mg/mL 0.2 mg/mL 0.1 mg/mL

Gadolinium concentration

Mean attenuation 120 kVp 2750

0 250 500 750 1000 1250 1500 1750 2000 2250 2500 2750

Energy (keV)

26.3 mg/mL 20.7 mg/mL 15.7 mg/mL 10.6 mg/mL 5.1 mg/mL 4.0 mg/mL 3.0 mg/mL 2.0 mg/mL 1.0 mg/mL 0.5 mg/mL 0.4 mg/mL 0.2 mg/mL 0.1 mg/mL

Gadolinium concentration

Mean attenuation 140 kVp

a

b

Fig 3 Mean CT attenuation

coefficients across all

monochromatic energies Mean

CT attenuation of all

measurements for each

gadolinium concentration,

constructed in steps of 10 keV.

Graphs were used to investigate

the ability of SDCT low

monochromatic energies to

visually identify an increase in

HU values due to the presence of

gadolinium-containing contrast

media Scans were performed at

120 kVp (a) and 140 kVp (b) For

subsequent gadolinium

quantification, only attenuation

profiles between 70 to 200 keV

were used for the

in-house-developed software analyses

(Fig 2c )

120 kVp

All gadolinium concentrations were overestimated Mean

measurement errors for the 15 ROIs per concentration ranged

between 0.1 and 2.4 mg/mL (Table1, Fig.5a) For each

con-centration, measurement errors at 120 kVp were significantly

(Bonferroni P < 0.004) higher compared to measurement

er-rors at 140 kVp, except for the lowest two concentrations of

0.1 and 0.2 mg/mL Relative measurement errors (%) were

below 10% down to 2.0 mg/mL true gadolinium

concentra-tions and increased up to 29.4% at 0.5 mg/mL and 100.9% at

0.1 mg/mL true gadolinium concentration (Table1, Fig.5b)

140 kVp

Per concentration (N = 15), mean measurement errors varied

from−0.2 to 0.4 mg/mL (Table1, Fig.5a) Relative

measure-ment errors (%) stayed below 5% down to 1.0 mg/mL true

gadolinium concentration At true gadolinium concentrations

between 0.1 and 0.5 mg/mL, mean measurement errors were low with 0.1 ± 0.0 mg/mL deviation; expressed in percentages this varied between 93.5 ± 26.8% and 14.1 ± 4.1% deviation, respectively (Fig.5b)

Attenuation coefficient Overall mean attenuation increased when lowering keV (Fig 3) At the lowest possible monochromatic energy (40 keV), mean attenuation for the estimated clinical gadolin-ium range of 0.5, 1.0, 2.0, 3.0, 4.0 and 5.1 mg/mL yielded 28,

74, 164, 260, 349 and 416 HU at 120 kVp and 34, 84, 180,

284, 382 and 464 HU at 140 kVp, respectively

Discussion This study showed that it is feasible to quantify a commonly clinically encountered range of gadolinium concentrations in a

-0.2 -0.15 -0.1 -0.05 0 0.05 0.1

0.15

Interscan variation 120 kVp

+1.96 SD

-1.96 SD

0.08

-0.08 Scan 1

Scan 2 Scan 3 Scan 4 Scan 5

Mean gadolinium concentration measurement of all observers

-0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3

Mean gadolinium concentration measurement of all observers

Interscan variation 140 kVp

Scan 1 Scan 2 Scan 3 Scan 4 Scan 5

-1.96 SD

+1.96 SD

-0.14 0.14

a

b

Fig 4 Interscan agreement for all

scan repetitions at 120 kVp (a)

and 140 kVp (b) Values are

plotted according to Jones et al.

[ 40 ] The measurement difference

of each scan compared to the

mean measurement of all scans is

plotted against the mean

measurement of all scans

phantom model with overall high accuracy and reproducibility

using an in-house-developed material decomposition method

on a novel clinical dual-layer spectral detector CT system

Whereas conventional CT displays anatomical structures

as a function of tissue density, DECT enables enhanced tissue

characterization using MDI Quantitative assessment of

con-trast agent uptake and its provided distribution map can be

used as a surrogate for tissue perfusion [6–12,14] In the

current study we showed that clinically encountered low

con-centrations of gadolinium, down to 0.5 mg/mL, can be

accu-rately quantified with a mean measurement error of 0.1 mg/

mL using SDCT at both 120 and 140 kVp In the ultra-low

gadolinium concentration range (0.1–0.4 mg/mL), expected to

be encountered in tissues with a perfusion defect, the mean

measurement error remained around 0.1 mg/mL at both 120

and 140 kVp However, at these low concentrations the

mar-gin of error increased substantially and approached the

gado-linium concentration itself, indicating that the lower limit of

reasonably accurate gadolinium quantification using SDCT

lies between 0.5 and 1.0 mg/mL In the range of clinically

encountered gadolinium concentrations (0.5–5.1 mg/mL)

af-ter administration of 0.1–0.2 mmol/kg bodyweight, mean CT

numbers at 40 keV ranged between 28 and 464 HU (Fig.3)

The combination of high(er) attenuation at lower

monochro-matic energies and accurate quantification of low gadolinium

concentrations opens up the possibilities for DECT scanning

with the use of gadolinium as a contrast agent Potential

clin-ical applications include detection of myocardial [6–12] and

pulmonary perfusion defects [14–16] and the characterization

of tumour masses and therapy response assessment [17–19]

In clinical routine, adequate tissue contrast and contrast agent density maps are important for the diagnosis and evalu-ation of organ perfusion defects However, to be able to create

a gadolinium density map as a surrogate for tissue perfusion, accurate gadolinium quantification is essential, as the post-processing is based on these measurements This is the first study to describe the accuracy of gadolinium quantification using MDI on SDCT Gabbai et al [28] described the capabil-ity of spectral differentiation of gadolinium using SDCT, which is in accordance with our study However, no quantita-tive values were described and high concentrations (4.7– 187.6 mg/mL) of gadolinium were used, which is at least one

to two orders of magnitude above the estimated range encoun-tered in healthy cardiac, lung, liver, spleen and kidney tissue (0.58–4.66 mg/mL) Zhang et al [30] showed a high sensitiv-ity and specificsensitiv-ity for gadolinium-enhanced dual-source DECT pulmonary angiography to detect pulmonary embolism

in rabbits However, as in the study by Gabbai et al gadolin-ium concentration was not quantified In addition, high intra-venous doses of gadolinium contrast agent, 1.5 and 2.5 mmol/

kg bodyweight, were administrated Bongers et al [31] evalu-ated the potential of gadolinium as a CT contrast agent using dual-source DECT in a phantom setup In accordance with our study they found that monochromatic images at low energy (e.g 40 keV) allow for higher attenuation Additional quanti-fication was performed by using the material-specific

dual-Table 1 Mean errors of

gadolinium concentration

measurements with a dual-layer

spectral detector CT scanner

True concentration (mg/mL) 120 kVp 140 kVp

Measurement error Measurement error

Data are given as mean ± standard deviation For each true concentration 15 measurements were done at both 120 and 140 kVp

*Significantly (Bonferroni P < 0.004) higher compared to measurement error at 140 kVp

energy ratio for gadolinium For the true gadolinium

concen-trations 6.3, 3.2, 1.6, 0.8, 0.4 and 0.2 mg/mL relative

measure-ment errors were 11.5, 12.0, 21.6, 21.6, 104.2 and 159.4%,

respectively In our study we found a higher accuracy with

relative measurement errors of less than 10% down to

2.0 mg/mL at 120 kVp and 1.0 mg/mL at 140 kVp A possible

explanation for this difference can be found in the algorithm

The post-processing algorithms used by Bongers et al [31] was

originally designed for iodine, whereas our algorithm was

spe-cifically designed for gadolinium quantification

We found a slightly lower measurement error, and thus

higher accuracy, for scans acquired at 140 kVp compared to

120 kVp When scanning with a higher tube voltage, more

high energy X-ray photons are produced This decreases the

spectral overlap between high- and low-energy spectra, and

thereby improves the accuracy of material decomposition,

which is in accordance with the findings of Gabbai and

col-leagues [28] Moreover, 140 kVp acquisitions resulted in

higher CT numbers of different gadolinium concentrations at

monochromatic 40 keV images (34–464 HU) compared to

120 kVp acquisitions (28 to 416 HU), indicating a superior spectral separation at a higher tube voltage

Even though gadolinium chelates are generally considered to

be safe contrast agents, with acute reaction rates of approximately 0.001–0.07% [41], recently concerns have arisen about their long-term safety after the discovery that administration of multi-ple doses has led to detectable gadolinium levels in the brain [42,

43] In addition, gadolinium contrast has been linked to an in-creased risk of nephrogenic systemic fibrosis (NSF) in patients with impaired renal function [44] In both conditions the linear non-ionic and linear ionic contrast agents have primarily been implicated, whereas macrocyclic gadolinium agents, such as used in the current study, have not been linked conclusively to either of these conditions [45–47] Although both iodine and gadolinium contrast agents pose a risk for patients with impaired renal function, gadolinium is thought to be preferred in patients with renal failure and a glomerular filtration rate greater than

30 mL/min since the risk of NSF is low in these patients, while the risk of iodine contrast-induced nephropathy clearly exists [41] Furthermore, using gadolinium could potentially obviate the need for pre- and post-imaging hydration as well as premedication protocols that are commonly used in patients with impaired renal function who undergo contrast-enhanced CT scanning, or patients with known allergies to iodinated contrast agents In the current study a relatively simple method for mate-rial decomposition using in-house-developed software is pro-posed Our method is based on the mass attenuation coefficient across monochromatic energies Monochromatic reconstructions take into account the function of two independent factors: the photoelectric and the Compton effect [2] The photoelectric effect

is strongly related to the atomic number of a material in the CT energy range and is therefore material-specific [37] Our method takes into account this material-specific effect by evaluating the attenuation across monochromatic energies

The strength of our study is that we evaluated accuracy of gadolinium quantification in an optimal controlled setting with a wide and clinically relevant range of gadolinium con-centrations, which provides the basis for further research and clinical applications Our study also has some limitations The most important is that we used a static phantom in which organ motion was not taken into account In addition, a fixed concentration is not the same as a bolus injection However,

we tried to mimic the clinical situation as best as possible by using low concentrations of gadolinium, which are expected

to be typically encountered clinically A second limitation is that our study only takes into account water and gadolinium when calculating the amount of gadolinium concentration Since human tissue does not only consist of water and gado-linium, future phantom and patient research will have to ad-dress (healthy) tissue attenuation as well using a three- or multi-material decomposition method A third limitation is the need for relatively high peak tube voltage (120 or

a

b

Fig 5 Accuracy of gadolinium quantification Accuracy expressed as

mean measurement error (a) and mean relative measurement error (b).

Symbol represents mean and error bar the standard deviation

140 kVp) settings to ensure sufficient spectral separation.

However, the higher radiation dose due to the use of

high kVp acquisitions can be addressed by reducing tube

cur-rent (mAs) The fourth limitation is that we only evaluated one

DECT technique; therefore, our results may be limited to the

vendor used in this study

In conclusion, SDCT allows for accurate quantification of

commonly clinically used gadolinium concentrations at both

120 and 140 kVp Lowest measurement errors were found for

140 kVp acquisitions

Compliance with ethical standards

Guarantor The scientific guarantor of this publication is Prof T.

Leiner.

Conflict of interest The authors of this manuscript declare

relation-ships with the following companies: Alain Vlassenbroek and Julien

Milles are employees of Philips Healthcare.

The other authors of this manuscript declare no relationships with any

companies whose products or services may be related to the subject

mat-ter of the article.

Funding The authors state that this work has not received any funding.

Statistics and biometry One of the authors has significant statistical

expertise.

Informed consent Written informed consent was not required because

this concerns a phantom study.

Ethical approval Institutional review board approval was not required

because this concerns a phantom study.

Methodology

• prospective

• experimental

• performed at one institution

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